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SSRN 2023年

作者： do Nascimento, Amanda Roseane Farias Greati, Victor Rodrigues Leopoldino, Ramon Weyler Duarte Martins, Rand Randall Barbosa, Euzebio Guimarães Graduate Program in Pharmaceutical Sciences Health Sciences Center Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Graduate Program in Systems and Computing Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Introduction: The intensive care of neonates is associated with the entry of a large volume of data in their medical records. The treatment of this data can be done through Machine Learning: a tool capable of assisting in the detection and decision-making of a wide range of medical conditions, including adverse drug reactions (ADR). Objective: To train a prediction model to assist in the detection of adverse drug reactions in neonates admitted to an ICU. Methodology: observational study developed in the Neonatal Intensive Care Unit of a teaching hospital in Brazil. Clinical data were collected from daily pharmacotherapeutic follow-up, processed and analyzed by machine learning through libraries written in Python language. Results: Eight hundred and three neonates were included in the study, with a mean gestational age of 32.2 ± 4.2 weeks and mean birth weight of 1807.2 ± 936.6g. The incidence of ADR was 10.8%. Antimicrobials, especially aminoglycosides, were the most prescribed drugs for this population. An algorithm was trained and tested to predict ADR in the NICU, in which the metrics were precision (0.35) and recall (0.823), with specificity (80%) and sensitivity (67%). Conclusion: There is a high potential in the machine learning method to predict ADR in neonates admitted to an ICU. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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ETHICS-2023 Session D1 - Open Forum: Building a Technology Ethics Community

ETHICS-2023 Session D1 - Open Forum: Building a Technology E...

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IEEE International Symposium on Ethics in Science, Technology and Engineering

作者： Thomas Creely Marc Cheong Heather Love Ketra Schmitt Ethics and Emerging Military Technology Graduate Program U.S. Naval War College School of Computing and Information Systems & Centre for AI and Digital Ethics (CAIDE) University of Melbourne English Language and Literature University of Waterloo Concordia University

This session was an open forum where audience members were invited to participate in discussions of a number of themes with relevance to ethics and technology and to future conferences in the IEEE ETHICS series. The discussions took place in small groups, with groups reporting back to the full cohort for collaborative brainstorming.

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Strengths,weaknesses,opportunities,and threats(SWOT)analysis of supercapacitors:A review

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Journal of Energy Chemistry 2023年第4期79卷 611-638页

作者： Pragati A.Shinde Qaisar Abbas Nilesh R.Chodankar Katsuhiko Ariga Mohammad Ali Abdelkareem Abdul Ghani Olabi Sustainable Energy&Power Systems Research Centre RISEUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates International Center for Materials Nanoarchitectonics(WPI-MANA) National Institute for Materials Science(NIMS)1-1 NamikiTsukuba 305-0044Japan Department of Energy and Material Engineering Dongguk University-SeoulSeoul 04620Republic of Korea Graduate School of Frontier Sciences The University of Tokyo5-1-5 KashiwanohaKashiwaChiba 277-8561Japan Mechanical Engineering and Design School of Engineering and Applied ScienceAston UniversityAston TriangleBirmingham B47ETUK School of Computing Engineering and Physical SciencesUniversity of The West of ScotlandGlasgowPA12BEUK

The development of clean and sustainable energy sources has received widespread interest in the past few decades due to the rolling energy demands while extenuating the rising tiers of greenhouse gases and environmental *** to their intermittent nature,these green and sustainable sources require appropriate energy storage *** different energy storage technologies,electrochemical energy storage devices,particularly supercapacitors(SCs),have fascinated global attention for their utilization in electric vehicles,power supports,portable electronics,and many others application requiring electric energy devices for their ***,the growth of SCs in the commercial market has squeezed requirements,and further developments are obligatory for their effective *** the meantime,SCs also face technical complications and contests for their introduction in industrial settings because of their low energy density and high Levelized *** present study combines core strengths,weaknesses,opportunities,and threats(SWOT)analysis of SCs with new perspectives and recent *** challenges and the future progressive prospects of SCs are also presented in *** review will afford consistent direction and new superhighways for the further development of SCs as standalone and complementary energy storage systems.

关键词： Supercapacitors Energy storage Strengths Opportunities

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Query-Efficient Video Adversarial Attack with Stylized Logo

arXiv

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arXiv 2024年

作者： Tang, Duoxun Cao, Yuxin Xiao, Xi Wang, Derui Wen, Sheng Zhu, Tianqing The College of Science Sichuan Agricultural University Ya’an China The School of Computing National University of Singapore Singapore The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The Cybersecurity and Quantum Systems Group CSIRO’s Data61 Australia The School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC Australia The Faculty of Data Science City University of Macau China

Video classification systems based on Deep Neural Networks (DNNs) have demonstrated excellent performance in accurately verifying video content. However, recent studies have shown that DNNs are highly vulnerable to adversarial examples. Therefore, a deep understanding of adversarial attacks can better respond to emergency situations. In order to improve attack performance, many style-transfer-based attacks and patch-based attacks have been proposed. However, the global perturbation of the former will bring unnatural global color, while the latter is difficult to achieve success in targeted attacks due to the limited perturbation space. Moreover, compared to a plethora of methods targeting image classifiers, video adversarial attacks are still not that popular. Therefore, to generate adversarial examples with a low budget and to provide them with a higher verisimilitude, we propose a novel black-box video attack framework, called Stylized Logo Attack (SLA). SLA is conducted through three steps. The first step involves building a style references set for logos, which can not only make the generated examples more natural, but also carry more target class features in the targeted attacks. Then, reinforcement learning (RL) is employed to determine the style reference and position parameters of the logo within the video, which ensures that the stylized logo is placed in the video with optimal attributes. Finally, perturbation optimization is designed to optimize perturbations to improve the fooling rate in a step-by-step manner. Sufficient experimental results indicate that, SLA can achieve better performance than state-of-the-art methods and still maintain good deception effects when facing various defense methods. We believe that the proposed SLA can raise awareness among the security community about the reliability and security of video classification systems and serve as a memorandum of possible attack methods for the future. Copyright © 2024, The Authors. All rights

关键词： Budget control

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Robust Wireless Distributed Learning Empowered by Thz Communications Data for Internet of Unmanned Vehicles Agents: Efficient Cluster Driving Decision-Making

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IEEE Internet of Things Journal 2025年

作者： Li, Zihong Wu, Jun Bashir, Ali Kashif Li, Xingwang Waseda University Graduate School of Information Production and Systems Fukuoka8080135 Japan Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Henan Polytechnic University School of Physics and Electronic Information Engineering Henan Jiaozuo454150 China

With the rapid development of the Internet of Unmanned Vehicles Agents (IUVA), efficient and secure communication has become a key requirement. However, unstable wireless channel conditions pose several challenges to existing Wireless Distributed Learning (WDL) in the IUVA environment. First, the parameter transmission of WDL in the IUVA will be interfered by dynamic changes in vehicle position, which will affect the training accuracy and the loss of the learning model. Second, increased communication overhead due to the large amount of data generated by vehicles sensors, and third is the complexity of making real-time driving decisions with diverse vehicle data. This paper presents an innovative WDL framework based on Terahertz (Thz) communication technology, addressing communication and data processing challenges in the IUVA environment. Our framework designs a Thz communication encoding method, treating each vehicle as a local model node participating in the WDL process. First, we established a IUVA cluster based on Thz communication, addressing the current issues of high latency and low efficiency in IUVA communications. Second, we designed a WDL framework where vehicles within the IUVA act as distributed learning participants, reducing communication overhead in IUVA wireless communication. Finally, our proposed wireless distributed driving decision-Making model leverages the physical parameters of participating vehicles to derive collective driving decisions for the IUVA cluster, enhancing the accuracy of IUVA driving decisions. Overall, the framework proposed in this paper provides a new approach for achieving efficient and secure IUVA communication and contributes significantly to intelligent unmanned decision-making in IUVA. © 2025 IEEE.

关键词： Highway administration

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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Refining ADHD Diagnosis with EEG: The Impact of Preprocessing and Temporal Segmentation on Classification Accuracy

arXiv

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arXiv 2024年

作者： García-Ponsoda, Sandra Maté, Alejandro Trujillo, Juan Lucentia Research Group Department of Software and Computing Systems University of Alicante Rd. San Vicente s/n San Vicente del Raspeig03690 Spain ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence Camí de Vera s/n Valencia46022 Spain

Background: EEG signals are commonly used in ADHD diagnosis, but they are often affected by noise and artifacts. Effective preprocessing and segmentation methods can significantly enhance the accuracy and reliability of ADHD classification. Methods: We applied filtering, ASR, and ICA preprocessing techniques to EEG data from children with ADHD and neurotypical controls. The EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using various EEG segments and channels with Machine Learning models (SVM, KNN, and XGBoost) to identify the most effective combinations for accurate ADHD diagnosis. Results: Our findings show that models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD. The highest classification accuracy (86.1%) was achieved using data from the P3, P4, and C3 channels, with key features such as Kurtosis, Katz fractal dimension, and power spectrums in the Delta, Theta, and Alpha bands contributing to the results. Conclusions: This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. The results suggest that further research on cognitive fatigue and segmentation could enhance diagnostic accuracy in ADHD patients. © 2024, CC BY-NC-SA.

关键词： Diseases

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Exact Cluster Dynamics of Indirect Reciprocity in Complete Graphs

arXiv

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arXiv 2024年

作者： Bae, Minwoo Shimada, Takashi Baek, Seung Ki Department of Physics Pukyong National University Busan48513 Korea Republic of Department of Systems Innovation Graduate School of Engineering The University of Tokyo Bunkyo-ku 7-3-1 Hongo Tokyo113-8656 Japan Department of Scientific Computing Pukyong National University Busan48513 Korea Republic of

Heider's balance theory emphasizes cognitive consistency in assessing others, as is expressed by "The enemy of my enemy is my friend." At the same time, the theory of indirect reciprocity provides us with a dynamical framework to study how to assess others based on their actions as well as how to act toward them based on the assessments. Well-known are the 'leading eight' from L1 to L8, the eight norms for assessment and action to foster cooperation in social dilemmas while resisting the invasion of mutant norms prescribing alternative actions. In this work, we begin by showing that balance is equivalent to stationarity of dynamics only for L4 and L6 (Stern Judging) among the leading eight. Stern Judging reflects an intuitive idea that good merits reward whereas evil warrants punishment. By analyzing the dynamics of Stern Judging in complete graphs, we prove that this norm almost always segregates the graph into two mutually hostile groups as the graph size grows. We then compare L4 with Stern Judging: The only difference of L4 is that a good player's cooperative action toward a bad one is regarded as good. This subtle difference transforms large populations governed by L4 to a "paradise" where cooperation prevails and positive assessments abound. Our study thus helps us understand the relationship between individual norms and their emergent consequences at a population level, shedding light on the nuanced interplay between cognitive consistency and segregation dynamics. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Privacy-Preserving EEG Signal Analysis with Electrode Attention for Depression Diagnosis: Joint FHE and CNN Approach

Privacy-Preserving EEG Signal Analysis with Electrode Attent...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Huanze Dong Jun Wu Ali Kashif Bashir Qianqian Pan Marwan Omar Anwer Al-Dulaimi Graduate School of Information Production and Systems Waseda University Japan Department of Computing and Mathematics Manchester Metropolitan University Manchester U.K. Information Technology and Management Illinois Institute of Technology USA 5G Center of Excellence EXFO Canada

Artificial intelligence has been utilized to analyze patients' electroencephalograms (EEG) to diagnose depression. However, attackers can deduce patients' privacy after analyzing patients' EEG time series. Therefore, researchers propose to operate ciphertext calculation in depression diagnosis models based on homomorphic encryption. Nevertheless, homomorphic encryption requires consistent private keys during training, which could result in other participants decrypting the ci-phertexts. Additionally, existing EEG-based models neglect the relationship among electrode positions during EEG acquisition. To address these issues, we propose a novel training strategy for the depression diagnosis model based on fully homomorphic en-cryption (FHE) and electrode topology. Specifically, we establish a training strategy that prioritizes the privacy of patients' EEG data without compromising the cost-effectiveness of the diagnosis model. Furthermore, we incorporate the attention mechanism of electrode topology into our model to improve its performance and verify the relationship among topology locations. Our proposed model outperforms the original convolution neural network model, achieving higher accuracy in depression diagnosis and identifying virtual electrode channels for the first time.

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